论文标题
radixspline：单次学习指数
RadixSpline: A Single-Pass Learned Index
论文作者
论文摘要
最近的研究表明，学到的模型可以胜过大小和查找性能的最先进索引结构。尽管这是一个非常有前途的结果，但现有的学习结构通常很麻烦，并且建造速度很慢。实际上，我们知道的大多数方法都需要对数据进行多次培训。 我们介绍了RadixSpline（RS），这是一个可以在数据上单个通过中构建的学习索引，并且与最先进的索引模型（如RMI）具有竞争力，尺寸和查找性能。我们使用SOSD基准评估RS，并证明它在所有数据集上都能在所有数据集上获得竞争性结果，尽管它只有两个参数。
Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.
